import sys
import numpy as np
import pandas as pd
import seaborn as sns
sns.set_theme()

results_folder = 'mmvec_major_taxa_scrambled_2'
results_base_name = 'latent_dim_3_input_prior_1.00_output_prior_1.00_beta1_0.90_beta2_0.95'

table = pd.read_table(results_folder + '/' + results_base_name + '_ranks.txt', index_col=0)
table.head()

	Propionibacteriaceae	Staphylococcus caprae or capitis	Staphylococcus epidermidis	Staphylococcus hominis	Other Staphylococci	Polyomavirus HPyV6	Polyomavirus HPyV7	Merkel Cell Polyomavirus	Malasseziaceae	Corynebacteriaceae	Micrococcaceae	Other families
featureid
X940001	0.184421	0.053692	0.153110	0.199588	-0.032911	-0.020608	-0.037587	0.110812	0.084744	-0.016153	0.145283	-0.061960
X940002	-0.070998	-0.077200	-0.037909	-0.008117	-0.079228	-0.080866	-0.150118	-0.134636	-0.085292	-0.077377	-0.008113	-0.081268
X940005	-0.020409	-0.113338	-0.017493	-0.000518	-0.116096	-0.107599	-0.377870	-0.157181	-0.151141	-0.094980	0.057534	-0.135563
X940007	0.095343	0.247376	0.343342	0.376841	0.278461	0.203163	0.406508	0.115586	0.326311	0.269885	0.322992	0.287431
X940010	1.102840	0.815784	-0.069650	0.356785	0.513313	1.011083	0.652279	0.737868	0.371052	0.363324	0.048634	0.631470

table['Selected'] = np.isin(table.index,
                            ['X940203', 'X940589', 'X940625', 'X940925', 'X940936', 'X942191',
                             'X942237', 'X950023', 'X950028', 'X950056', 'X950157', 'X950173',
                             'X950193', 'X950225', 'X950228', 'X950233', 'X950254', 'X950396',
                             'X950485', 'X950584', 'X950661', 'X950999', 'X960035', 'X960242',
                             'X960306', 'X960421', 'X960463', 'X960465', 'X960712', 'X960726',
                             'X960934', 'X961553', 'X961686', 'X970018', 'X970091', 'X970092',
                             'X970232', 'X970283', 'X970327', 'X970342', 'X970633', 'X970680']
                           )
table.sort_values('Selected', inplace=True)
sns.relplot(
    table,
    y='Propionibacteriaceae', x='Staphylococcus epidermidis', hue='Selected'
)

<seaborn.axisgrid.FacetGrid at 0x7f478b89b3d0>

sns.pairplot(table, hue='Selected')

<seaborn.axisgrid.PairGrid at 0x7f478b40dbd0>

for i in table.columns[:-1]:
    sns.displot(table, x=i, hue='Selected', multiple='stack')